International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Enhancing Fisheye State Routing Performance in MANETs Using SDN: An Evaluation Under Varying Node Mobility Patterns

Tuhfa Sabry MahmoodDepartment of Computer Science, University of Mosul, Mosul-IraqManarYounis AhmedUniversity of Ninevah, Mosul-Iraq

Vol 9 No 6 (2025): Volume 9, Issue 6, June 2025 | Pages: 272-280

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 04-07-2025

doi Logo doi.org/10.47001/IRJIET/2025.906036

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Abstract

Mobile Ad Hoc Networks (MANETs) are wireless systems which have self-organizing with random topologies, where node moving significantly impacts routing performance. Fisheye State Routing (FSR) is a proactive protocol that designed to reduce routing overhead, later it explained under highly random mobility patterns. This study merges the impact of four distinct mobility patterns (Random, Deterministic, Directed, and Network-Based) on FSR performance and explores enhancements by merging Software Defined Networking (SDN). Simulations were conducted using the NetLogo environment, tests key performance metrics: end-to-end delay, packet loss, delivery ratios, throughput, and routing overhead. Findings demonstrate that SDN enhanced FSR significantly outperforms traditional FSR across all moving models, with improvements of up to 12.5% in delay reduction and 20% in throughput. This research highlights the chance of SDN based architectures in beneficent adaptive routing for moving intensive MANET environments.

Keywords

MANET, Fisheye State Routing (FSR), Software-Defined Networking (SDN), Mobility Patterns, NetLogo Simulation, Routing Protocols, Network Performance


Citation of this Article

Tuhfa Sabry Mahmood, & ManarYounis Ahmed. (2025). Enhancing Fisheye State Routing Performance in MANETs Using SDN: An Evaluation Under Varying Node Mobility Patterns. International Research Journal of Innovations in Engineering and Technology - IRJIET, 9(6), 272-280. Article DOI https://doi.org/10.47001/IRJIET/2025.906036

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Abdal, M. M. M., & Abdullah, D. B. (2021). Towards real-time secure of IoT using SDN. *AIP Conference Proceedings*, 3211(1), 030036. https://doi.org/10.1063/5.0055044
  2. Adanigbo, A., Ibraheem, A., & Okafor, A. (2025). Energy-Efficient Routing in MANETs Using AI-based SDN Frameworks: A Survey. Journal of Wireless Networks and Communications, 13(1), 22–35.
  3. Alash, M. (2016). Performance Comparison of MANET Routing Protocols Under Dynamic Topologies. International Journal of Wireless Networks, 8(4), 211–218.
  4. Ali, H. D., & Abdulqader, A. H. (2021, August). Using software defined network (SDN) controllers to enhance communication between two vehicles in vehicular ad hoc network (VANET). In 2021 7th international conference on contemporary information technology and mathematics (ICCITM) (pp. 106-111). IEEE.‏
  5. Bakht, H. (2011). Survey of Routing Protocols in Mobile Ad-Hoc Networks. In Proceedings of the 2nd International Conference on Networking and Telecommunications (pp. 1–7).
  6. Bai, F., & Helmy, A. (2004). A Survey of Mobility Models in Wireless Adhoc Networks. University of Southern California.
  7. Fazio, M., Villari, M., &Puliafito, A. (2023). Spectral Analysis of Mobility Patterns in Crowded Spaces for Smart Services. IEEE Internet of Things Journal, 10(2), 1203–1214.
  8. Hadeed, W. (2022). The Impact of the Relation Between Movement Patterns and Nodes Distribution on the Performance of Smart Cities. Technium, 4(3).‏
  9. Hasan, M. M., Ullah, F., & Islam, S. (2020). Performance Metrics Evaluation of Routing Protocols in MANETs. Journal of Computer Science, 16(6), 841–854.
  10. Hussein, A., & Kadhim, A. (2010). The Effect of Mobility Models on MANET Performance Using NS2. Journal of Mobile Networks, 3(1), 45–52.
  11. Kumar, R., & Singla, R. K. (2022). Energy Analysis of MANET Routing Protocols Under Mobility. Journal of Wireless Mobile Networks, 11(3), 88–94.
  12. Mohammed, G. A., & Aldabbagh, O. A. I. (2023). A Comparative Evaluation of the Performance of SDN Controllers (ONOS) using DOCKER Container. Technium, 8.‏
  13. Mundher, Z. A. (2022). A Method for Investigating Coverage Area Issue in Dynamic Networks. Technium Romanian Journal of Applied Sciences and Technology, 4(6), 72–80. https://www.researchgate.net/publication/360011072
  14. Nemade, R., & Pujeri, R. (2023). Mobility-aware Performance Evaluation of DSR, AODV, and DSDV in MANETs. Wireless Networks, 29(2), 389–403.
  15. Persson, M., Al-Bayatti, A. H., & Othman, M. (2003). Enhancing FSR with Situational Awareness in Highly Dynamic Networks. Ad Hoc Networks, 1(3), 311–325.
  16. Rahman, M., Chowdhury, A., & Jahan, M. (2024). DistB-VNET: Distributed Blockchain-based Secure VANET Architecture Integrating SDN and NFV. Vehicular Communications, 40, 100642.
  17. Razouqi, M., Al-Emran, M., & Naser, N. (2024). Performance Analysis of Routing Protocols Under Irregular Mobility Patterns in MANETs. Journal of Network and Computer Applications, 207, 103575.
  18. Sathish, K., & Ramesh, D. (2011). Performance Comparison of Routing Protocols in Static MANET Environments. International Journal of Computer Networks, 4(5), 18–25.
  19. Shujairi, Z., Al-Ali, M., & Hadi, A. (2025). A Deep Reinforcement Learning-based SDN Routing Framework for IoT in Healthcare Applications. IEEE Access, 13, 56023–56038.
  20. Sun, Y. (2000). Fisheye State Routing Protocol (FSR) for Ad Hoc Networks. IETF.
  21. Wang, J., & Zhang, H. (2024). Routing Protocol Classification Based on Topology Awareness in MANETs. ACM Computing Surveys, 56(3), Article 47.
  22. Yang, Y., Chen, L., & Wang, Z. (2025). SDN-DRLTE: A Deep Learning-based Congestion Control Protocol for Mobile Networks. Computer Communications, 211, 108–117.
  23. Yoshimura, T., & Fujita, M. (2024). Path Pattern Analysis of Museum Visitors Using Symbolic Trace Sequences. Journal of Urban Data Analytics, 12(1), 23–37. 

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